烟幕在与精确制导**的对抗中具有高费效比，是光电对抗最有效的手段之一。如何有效评估烟幕的干扰效能是烟幕领域的重要研究方向。目前已有学者提出采用基于图像相关性指标评估烟幕的干扰效果，但大多根据烟幕干扰前后相关度变化对评价指标进行分析，未考虑实战中导引系统匹配、跟踪的性能，评价指标的适用性有待考证。为此，文中通过分析烟幕对目标图像的影响因素，综合考虑烟幕对图像相关度与目标匹配性能的影响，提出了一种基于仿真与外场试验的图像相关性指标筛选方法。试验验证结果表明，皮尔逊相关系数（Pearson Linear Correlation Coefficient, PLCC）与结构相似度（Structural Similarity Index Measure, SSIM）均能定量评估烟幕干扰效果，评价结果可为发烟器材的研制与运用提供辅助决策。

Overview: To address the challenges of traditional smoke detectors in identifying replaced smoke in the cargo hold of high-altitude, low-pressure aircraft, an innovative image-based fire-and-smoke detection system was developed using the domestic RK3588 embedded AI chip. This system employs an enhanced YOLOv5s detection algorithm tailored specifically for fire-and-smoke detection, incorporating several critical improvements to achieve high precision and operational efficiency. The backbone network of YOLOv5s is replaced with the lightweight GhostNet architecture, which significantly reduces computational requirements and the model’s parameter size, making it highly suitable for deployment on embedded devices with limited resources. To enhance feature extraction, a collaborative attention module is integrated between the backbone and the feature aggregation network, ensuring that critical features are captured effectively for better detection outcomes. In addition, the C3 structure in the feature fusion network is substituted with the VoV-GSCSP module. This modification not only enhances the integration of multi-scale features but also reduces computational complexity, enabling the system to handle high-resolution images more efficiently. To further optimize the system’s performance, the Slim-ASFF module is inserted between the feature fusion network and the detection head. This addition improves the combination of feature maps across varying scales, ensuring accurate detection of both small and large fire-and-smoke instances. The regression loss function is also updated by replacing the standard loss function with Focal EIOU. This improvement addresses challenges related to aspect ratio variations in the original loss function, enhancing the system’s ability to identify positive samples while reducing false alarms effectively. Experimental results on a self-constructed fire-and-smoke dataset demonstrate the system achieves a 2.0% increase in mean Average Precision at a 0.5 threshold (mAP50) and a 2.2% improvement at 0.5:0.95 thresholds (mAP50:95). These results demonstrate the algorithm’s effectiveness under challenging conditions, such as low light and high turbulence environments, making it highly reliable for real-world applications. The hardware of this system is centered around the RK3588 embedded processing board, which interfaces with a CMOS image sensor for real-time data acquisition. The processing board includes an RTSP streaming server, enabling the host computer to access the visual interface via the onboard LAN and an assigned IP address. Testing in a simulated cabin of 15 m × 8 m × 4 m demonstrated reliable performance, with flame alarms triggered within 10 seconds and smoke alarms within 20 seconds. All functional indicators met rigorous design specifications, confirming the system as a scalable, efficient, and reliable solution for fire-and-smoke detection in aircraft cargo holds. By combining advanced deep learning techniques, lightweight architectures, and optimized hardware, this system ensures compliance with the stringent demands of real-time monitoring in airborne environments.

识别出工业环境中的烟雾排放行为对于规范和实时监督企业，以及环境保护都具有至关重要的意义。然而，识别工业排放烟雾具有很高的挑战性，一方面工业排放烟雾具有高透明度、高动态性等特点;另一方面烟雾的形状和尺寸可能会因环境、光照等因素而发生变化。目前主流的烟雾识别方法都是基于图像或视频的深度学习模型，但是基于图像的模型无法对视频中烟雾的动态特性进行有效的时序建模，同时基于视频的模型没有考虑烟雾形状多变的特性。将随机块移位（random patch shift，RPS）和可变形注意力（deformable attention，DA）引入Swin Transformer。RPS将传统的2D空间注意力转变为时空注意力，从而使用2D的自注意力计算对动态烟雾进行建模；DA通过自适应形变的方式使网络能够适应不同的烟雾形态和外观变化，提高网络的鲁棒性和泛化能力。在RISE数据集上的实验结果表明，本文方法能够在3个子集上分别达到0.85、0.86和0.84的F1分数，相比其他方法有0.01~0.06的提升。